Simulation To Establish Benchmark Outcome Measures
Bibliographic record
Abstract
Following the early experiences in aviation, medical simulation has rapidly evolved into one of the most novel educational tools of the last three decades. In addition to its use in training individuals or teams in crisis resource management, simulation has been studied as a tool to evaluate technical and non-technical skills of individuals as well as, more recently, entire medical teams. It is usually fairly difficult to obtain clinical reference data from critical events to refute claims that the management of actual events fell below what could reasonably be expected and we demonstrated the use of rank order statistics to calculate quantiles with confidence limits for management times of critical obstetrical events using data from realistic simulation. This approach could be used to describe the distribution of treatment times in order to assist in deciding what performance may constitute an outlier. It can also identify particular challenges of clinical practice and allow the development of educational curricula. While the information derived from simulation has to be interpreted with a high degree of caution for a clinical context, it may represent a further ‘added value’ or important step in establishing simulation as a training tool and to provide information that could be used in an appropriate clinical context for adverse events. Large amounts of data (such as from a simulation registry) would allow the calculation of acceptable confidence intervals for the required outcome parameters as well as actual tolerance limits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".